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An application deployment method, device and equipment based on a multi-reasoning engine system

A reasoning engine and application deployment technology, applied in the field of deep learning, can solve problems such as low application deployment efficiency, achieve the effect of improving application deployment efficiency, reducing professional threshold and workload

Active Publication Date: 2021-10-29
INSPUR SUZHOU INTELLIGENT TECH CO LTD
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of this application is to provide an application deployment method, device, device, and readable storage medium based on a multi-reasoning engine system to solve the problem of low application deployment efficiency due to manual selection of a suitable reasoning engine

Method used

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  • An application deployment method, device and equipment based on a multi-reasoning engine system
  • An application deployment method, device and equipment based on a multi-reasoning engine system
  • An application deployment method, device and equipment based on a multi-reasoning engine system

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Embodiment 1

[0053] The following introduces Embodiment 1 of the application deployment method based on the multi-reasoning engine system provided by this application, see figure 1 , Embodiment 1 includes:

[0054] S11. Obtain a source model for application deployment.

[0055] The above source model is a trained deep learning model that needs to be deployed in actual applications. Specifically, the file path of the source model is input, and the source model is read according to the file path. In order to ensure reliability, during the reading process, it is judged whether the path is correct and the file is readable.

[0056] S12. Convert the source model to each reasoning engine of the multi-reasoning engine system, and obtain a target model corresponding to each reasoning engine.

[0057] In this embodiment, the reasoning engine is used to implement optimization, conversion and reasoning evaluation of the source model. Specifically, before converting the source model to the inferen...

Embodiment 2

[0064] The second embodiment of the application deployment method based on the multi-reasoning engine system provided by the present application will be introduced in detail below. see figure 2 , Embodiment 2 specifically includes the following steps:

[0065] S21. Obtain a source model for application deployment;

[0066] S22. Determine the model type of the source model according to the file suffix of the source model;

[0067] S23. Call the loading method of the model type to load the source model to determine whether the source model can be loaded normally; if so, go to S24, otherwise it prompts that the model is wrong;

[0068] S24. Convert the source model to each reasoning engine of the multi-reasoning engine system, and obtain a target model corresponding to each reasoning engine;

[0069] S25. Perform inference evaluation on each target model to obtain the inference duration of each target model; select the target model with the shortest inference time as the targ...

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Abstract

This application discloses an application deployment method based on a multi-inference engine system. For a given source model, the method can automatically convert the source model to different inference engines, and perform inference evaluation on the converted model model, and finally Select the optimal inference engine based on the inference evaluation results for subsequent application deployment. It realizes the automatic evaluation of each reasoning engine in the system, reduces the professional threshold and workload of the reasoning engine selection process, and avoids developers who spend a lot of time and energy choosing the reasoning engine because they are not familiar with the reasoning engine, which helps to improve the application deployment efficiency. In addition, the present application also provides an application deployment device, device, and readable storage medium based on a multi-reasoning engine system, the technical effect of which corresponds to the technical effect of the above method.

Description

technical field [0001] The present application relates to the technical field of deep learning, and in particular to an application deployment method, device, device and readable storage medium based on a multi-reasoning engine system. Background technique [0002] With the development of deep learning, more and more deep learning frameworks have emerged. In the model development stage, Google's tensorflow and facebook's pytorch are the most widely used. However, when it comes to specific application deployment, considering the impact of performance, storage and other factors, most of them use reasoning engines such as caffe, onnx, tensorrt, and tvm for application deployment. Faced with many inference engines, how to select the most suitable and optimal inference engine for application deployment is a major difficulty in practical applications. [0003] Since different inference engines have different levels of support for operators, the acceleration performance is also d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/445G06N5/04
CPCG06F9/44526G06N5/04
Inventor 刘鑫
Owner INSPUR SUZHOU INTELLIGENT TECH CO LTD
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